{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_gan/python/contrib_utils.py:305: The name tf.estimator.tpu.TPUEstimator is deprecated. Please use tf.compat.v1.estimator.tpu.TPUEstimator instead.\n",
      "\n",
      "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_gan/python/contrib_utils.py:310: The name tf.estimator.tpu.TPUEstimatorSpec is deprecated. Please use tf.compat.v1.estimator.tpu.TPUEstimatorSpec instead.\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "TensorFlow version 1.15.3 has been patched using tfdeterminism version 0.3.0\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import os\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import toml\n",
    "\n",
    "from modules.plot import merge_line_legends\n",
    "from modules.utils import load, expected_indepencent_jaccard\n",
    "from tasks.totaling import TotalizeValid\n",
    "\n",
    "sns.set()\n",
    "sns.set_style('ticks')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# output dir\n",
    "out_dir = './results'\n",
    "os.makedirs(out_dir, exist_ok=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# visual settings\n",
    "evalmetrics = [\n",
    "    'tau',\n",
    "    'jaccard'\n",
    "]\n",
    "\n",
    "d_evalmetric_ylabel = {\n",
    "    'tau': \"Kendal's Tau\",\n",
    "    'jaccard': \"Jaccard index\"\n",
    "}\n",
    "\n",
    "metrics = [\n",
    "    'log_likelihood_kde',\n",
    "    'log_inception_score',\n",
    "    'fid',\n",
    "]\n",
    "\n",
    "d_metric_rename = dict((\n",
    "    ('log_likelihood_kde', 'Influence on ALL'),\n",
    "    ('log_inception_score', 'Influence on IS'),\n",
    "    ('fid', 'Influence on FID'),\n",
    "))\n",
    "\n",
    "d_metric_color = dict((\n",
    "    ('log_likelihood_kde', 'C2'),\n",
    "    ('log_inception_score', 'C1'),\n",
    "    ('fid', 'C0'),\n",
    "))\n",
    "\n",
    "d_metric_marker = dict((\n",
    "    ('log_likelihood_kde', 's'),\n",
    "    ('log_inception_score', 'D'),\n",
    "    ('fid', 'o'),\n",
    "))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# load config and pickled results\n",
    "# these results can be loaded only after finishing the experiment command written in \"Experiment 1: Estimation Accuracy\" of README.md\n",
    "conf_2d = toml.load(open('conf/2d_valid.toml', 'r'))\n",
    "conf_mnist = toml.load(open('conf/mnist_valid.toml', 'r'))\n",
    "task_2d = TotalizeValid(**conf_2d[TotalizeValid.__name__])\n",
    "task_mnist = TotalizeValid(**conf_mnist[TotalizeValid.__name__])\n",
    "\n",
    "dir_2d = task_2d.output().path\n",
    "dir_mnist = task_mnist.output().path\n",
    "\n",
    "d_metric_means = {\n",
    "    'tau': dict((\n",
    "        ('log_likelihood_kde', load(os.path.join(dir_2d, 'means_tau_log_likelihood_kde.pkl'))),\n",
    "        ('log_inception_score', load(os.path.join(dir_mnist, 'means_tau_log_inception_score.pkl'))),\n",
    "        ('fid', load(os.path.join(dir_mnist, 'means_tau_fid.pkl')))\n",
    "    )),\n",
    "    'jaccard': dict((\n",
    "        ('log_likelihood_kde', load(os.path.join(dir_2d, 'means_jaccard_log_likelihood_kde.pkl'))),\n",
    "        ('log_inception_score', load(os.path.join(dir_mnist, 'means_jaccard_log_inception_score.pkl'))),\n",
    "        ('fid', load(os.path.join(dir_mnist, 'means_jaccard_fid.pkl')))\n",
    "    ))\n",
    "}\n",
    "\n",
    "d_metric_stds = {\n",
    "    'tau': dict((\n",
    "        ('log_likelihood_kde', load(os.path.join(dir_2d, 'stds_tau_log_likelihood_kde.pkl'))),\n",
    "        ('log_inception_score', load(os.path.join(dir_mnist, 'stds_tau_log_inception_score.pkl'))),\n",
    "        ('fid', load(os.path.join(dir_mnist, 'stds_tau_fid.pkl')))\n",
    "    )),\n",
    "    'jaccard': dict((\n",
    "        ('log_likelihood_kde', load(os.path.join(dir_2d, 'stds_jaccard_log_likelihood_kde.pkl'))),\n",
    "        ('log_inception_score', load(os.path.join(dir_mnist, 'stds_jaccard_log_inception_score.pkl'))),\n",
    "        ('fid', load(os.path.join(dir_mnist, 'stds_jaccard_fid.pkl')))\n",
    "    ))\n",
    "}\n",
    "\n",
    "assert conf_mnist['TotalizeValid']['retrace_afters'] == conf_2d['TotalizeValid']['retrace_afters']\n",
    "retrace_afters = np.asarray(conf_2d['TotalizeValid']['retrace_afters'])\n",
    "\n",
    "assert conf_mnist['TotalizeValid']['nepochs'] == conf_2d['TotalizeValid']['nepochs']\n",
    "nepoch = conf_2d['TotalizeValid']['nepochs']\n",
    "\n",
    "assert conf_mnist['TotalizeValid']['ncfsgd'] == conf_2d['TotalizeValid']['ncfsgd']\n",
    "nsamp_target = conf_2d['TotalizeValid']['ncfsgd']\n",
    "\n",
    "assert conf_mnist['TotalizeValid']['jaccard_size'] == conf_2d['TotalizeValid']['jaccard_size']\n",
    "jaccard_size = int(conf_2d['TotalizeValid']['jaccard_size'] * nsamp_target)\n",
    "\n",
    "d_metric_random = {\n",
    "    'tau': 0,\n",
    "    'jaccard': expected_indepencent_jaccard(nsamp_target, jaccard_size)\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figs = []\n",
    "for evalmetric in evalmetrics:\n",
    "    fig, ax = plt.subplots(figsize=(6, 4))\n",
    "    ks = nepoch - np.asarray(retrace_afters)\n",
    "\n",
    "    for metric, mean, std in zip(d_metric_means[evalmetric].keys(), d_metric_means[evalmetric].values(),\n",
    "                                 d_metric_stds[evalmetric].values()):\n",
    "        ax.plot(ks, mean, label=d_metric_rename[metric], marker=d_metric_marker[metric], color=d_metric_color[metric])\n",
    "        ax.fill_between(ks, mean - std, mean + std, alpha=.1, color=d_metric_color[metric])\n",
    "    ax.plot(ks, np.ones_like(retrace_afters) * d_metric_random[evalmetric], '--', color='grey', label='Random')\n",
    "    ax.set_xlabel('# of tracing back epochs $\\it{k}$')\n",
    "    ax.set_ylabel(d_evalmetric_ylabel[evalmetric])\n",
    "\n",
    "    fig.subplots_adjust(bottom=0.2)\n",
    "    fig.savefig(os.path.join(out_dir, f'{evalmetric}_valid.pdf'))\n",
    "    figs.append(fig)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAacAAADnCAYAAAC6wJlAAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/d3fzzAAAACXBIWXMAAAsTAAALEwEAmpwYAAAL2klEQVR4nO3ca4wVdZ7H4W9z1aASoyRjFkSiI2q8MxrFyygKim4rbRSNUaMdOnHZiZINCV4SnfUajO4LdYm6b9SEiSw2YXWN0VXES5A1O8ZLItmoGehhhIBCdNJAQ0PvC2Ivyq0bGvk5Ps876pz6V53T/z6fqjrVNHR1dXUFAArpt793AAB+TJwAKEecAChHnAAoR5wAKEecAChHnAAoR5wAKEecAChHnAAoR5wAKEecAChHnAAoR5wAKEecAChHnAAoR5wAKEecAChHnAAoR5wAKEecAChHnAAoR5wAKEecAChHnAAoR5wAKEecAChHnAAoR5wAKEecAChHnAAoR5wAKEecAChHnAAoR5wAKEecAChHnAAoR5wAKEecAChHnAAoR5wAKEecAChHnAAoR5wAKEecAChHnAAoR5wAKEecAChHnAAoR5wAKEecAChHnAAoR5wAKEecAChHnAAoR5wAKEecAChHnAAoR5wAKEecAChHnAAoR5wAKEecAChHnAAoR5wAKEecAChHnAAoR5wAKEecAChHnAAoR5wAKEecAChHnAAoR5wAKEecAChHnAAoR5wAKEecAChHnAAoR5wAKEecAChHnAAoR5wAKEecAChHnAAoR5wAKEecAChHnAAoR5wAKEecAChHnAAoR5wAKEecAChHnAAoR5wAKEecAChHnAAoR5wAKEecAChHnAAoR5wAKEecAChHnAAoR5wAKEecAChHnAAoR5wAKEecAChHnAAoR5wAKEecAChHnAAoR5wAKEecAChHnAAoR5wAKEecAChHnAAoR5wAKEecAChHnAAoR5wAKEecAChHnAAoR5wAKEecAChHnAAoR5wAKEecAChHnAAoR5wAKEecAChHnAAoR5wAKEecAChHnAAoR5wAKEecAChHnAAoR5wAKEecAChHnAAoR5wAKEecAChHnAAoR5wAKEecAChHnAAoR5wAKEecAChHnAAoR5wAKEecAChHnAAoR5wAKEecAChHnAAoR5wAKEecAChHnAAoR5wAKEecAChHnAAoR5wAKEecAChHnAAoR5wAKEecAChHnAAoR5wAKEecAChHnAAoR5wAKEecAChHnAAoR5wAKEecAChHnAAoR5wAKEecAChHnAAoR5wAKEecAChHnAAoR5wAKEecAChHnAAoR5wAKEecAChHnAAoR5wAKEecAChHnAAoR5wAKEecAChHnAAoR5wAKEecAChHnAAoR5wAKEecAChHnAAoR5wAKEecKGn90k/T9uStWb/00/29K8B+0NDV1dXVlwO2/MeMfLvhu+2WDz3gkPzblTP3auxx48blqaeeyrHHHrvL5y1dujTTpk1LkjQ3N2fu3Llpbm7OhRdeuFfb/zn48ssvc9lll+XOO+/MzTff3L38iSeeyLp16zJjxowfPH/evHlZuHBhHn/88Z94T3du/dJPs3LOw+nq7EjDgMH51bV35sCjTuqTsc2hXRs9enQ+/PDDDBkyJIsXL85jjz2WjRs3ZuPGjRk2bFieffbZ9Ou3/45pF/7xz3n+1SX5eu36HH7ogblp4vG5YMyIPhnb3Ni10aNH59hjj+3++Tc2NmbKlCm54447cuKJJ+aGG27IvHnz8tBDD2X48OHp6OjIwIEDM2HChEyZMiUHHHBAr7Y3oK9fwI7CtKvl+8Lrr7+e0047Lffee2+SZO7cuT/Ztve3F198MWeddVZaW1t/EKefi23DlCRdnR1ZOefhPg1UT/yS51CSdHZ25rbbbsvzzz+f4447Lkny2WefpaGhYb/t08I//jlPzv04HZs2J0lWr12fJ+d+nCR9Fqie+CXPjRdeeCFDhgzZ5XPGjh3bfbD7zTff5O677860adPy1FNP9WpbvYrT239anLf+tKhXG9jW7xf8y04fu3DU2Px21Fk9HuvGG2/MiSeemI8++iirVq3KxIkTM3369Lz00kt57rnnsmXLlnz44Yd54okntltv26Ocbf+9atWqPPDAA/nqq6/S0dGRyy+/PLfeemuSrUdVV155ZRYtWpTVq1enubk5N9xwQ5KtZysPPvhgVq9enWTr0VRTU9Mux9vW5s2b8+ijj+bdd99Nkpx33nmZPn16+vfvnzvuuCODBg3K0qVLs3Llypx66qmZOXPmDj8kOjs78/LLL2f27NlpaWnJJ598kpNPPrnH7+m+9tdPFuavHy/Y6eObN7Rn0+plyY9O5rs6O7LiD/+cgcNGpv8BO/7FOPiUcTn45At6tT/m0M5D097ennXr1uXwww/vXnbCCSf06v3tjQX/05b/+qBtl8/532Vrs6lzyw+WdWzanMf//aO89t/Ldrre+DOPzLjfHNmr/TE3+uYg5LDDDsvMmTNz/vnn5/PPP8+vf/3rHq/b52dOP6UVK1Zk9uzZaW9vz8UXX5yrr746V1xxRZYtW7bDS1i7M2PGjEydOjVnnHFGNm7cmJtvvjknnXRSzjnnnCTJhg0bMmfOnCxfvjyNjY1pamrK4MGDM3Xq1EybNi0TJ05Mkqxdu7ZH431vzpw5WbJkSebNm5ckaWlpyZw5c3L99dcnST7//PM8++yzaWhoSFNTUxYtWrTdGEmycOHCjBw5MiNHjkxTU1NaW1tLxWl3Nn3zl+3C1K2rK5u++Uv6/92uL7n0ljm0Y0OHDs3kyZMzYcKEnHnmmTn99NPT2NiYI444olfvR1/6cZh2t3xvmRvbu+6667ov6z3yyCMZPXr0bl/30KFDM3LkyH0bp9+OOmu3ZzeT5/zDTh/7/bh/6s3mduvSSy9Nv379cvDBB+foo49OW1tbjjrqqD0aa926dfnggw+yZs2a7mXt7e358ssvu39Ql112WZJk+PDhOeSQQ7Jy5cps2bIlnZ2d3RMnSQ499NAejfe9999/P01NTRk0aFCS5Kqrrsobb7zRPXkuvvjiDB48OMnWo9e2trYdTp7W1tY0NTUlSSZNmpRJkyblrrvu6l53fzv45At2eXbz40t62+rr756+Zw7t3D333JNbbrklixcvzjvvvJOnn346ra2te/z+7Mq43+z+7Kb5gdezeu367ZYPO/TAPDz13D7fJ3Njez25rLcje3Jrw8/6zGnbD93+/ftn8+bNu12nf//+2bLl/4+0Ojq2fhBu2bIlDQ0NefHFFzNw4MAeb29np789Ga+nevI6v/7667z33ntZsmRJZs2alSRZv359XnvttVxxxRV7tf2fyoFHnZRfXXvndoHaV2FKzKHdGTFiREaMGJFrrrkmU6ZMyVtvvZVbbrllr/ZlT9008fgffOeUJIMH9s9NE4/fJ9szN/rGt99+m7a2tt3eaPJjfX7bzdADDunV8p/akUcemU8/3Xp78hdffJElS5YkSQ466KCMGTMmzzzzTPdzV6xY0X2dd2dGjRqVAQMG5NVXX+1etnbt2l6Nd/bZZ2f+/PnZtGlTNm3alPnz52fs2LG9el3z58/PJZdckoULF2bBggVZsGBBHnroobS2tvZqnP3t+0A1DNj6C7Mvw7Sn/lbn0Lba29vz3nvvdR/xfvfdd1m+fHmGDx++x2PurQvGjMjvrjklww49MA3Zesb0u2tO+UlvhtidX8Lc6I01a9bkrrvuytlnn51jjjmmV+v2+ZnT3t4uvq+1tLTk9ttvz5tvvpkTTjjhB1/yPvroo3n44YfT2NiYJBkyZEgefPDBDBs2bKfjDRgwILNmzcp9992XWbNmpaGhIc3NzZk0aVKPx7v22mvT1tbWfUnu3HPPzeTJk3v1uubNm7fdNfCLLroo9957b5YvX55k6yn5K6+80v341KlTM2jQoLz99ts5//zzu5dfddVV3bfK7g/fB2r1f/5rhv39P5YKU/K3O4e21dXVldmzZ+f+++/P4MGDs3nz5jQ2Nmb8+PF7PGZfuGDMiFIx+rFfwtzYnUWLFmXSpEnZsGFDBg0alPHjx6elpaXX4/T53zkBwN7yP0QAUI44AVCOOAFQjjgBUI44AVCOOAFQjjgBUI44AVCOOAFQjjgBUI44AVCOOAFQjjgBUI44AVCOOAFQjjgBUI44AVDO/wEeE307cCBozQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# collect lines to make one legend figure\n",
    "merge_line_legends(\n",
    "    figs=figs,\n",
    "    line_names=[d_metric_rename[x] for x in metrics],\n",
    "    out_path=os.path.join(out_dir, 'legend_valid.pdf'),\n",
    "    ncol=4\n",
    ")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}